Start Here: Why Synchronism?
Physics has a fragmentation problem. Synchronism asks whether one principle could connect what we currently treat as separate domains.
Beginner Path · step 1.
What changes if this works? If one density function really spans quantum to galactic scales, two things become possible that aren't now: (1) a single measurable quantity (density) predicts behavior in domains currently requiring separate frameworks — fewer free parameters, more cross-domain predictions; (2) the boundary between “quantum” and “classical” becomes a calculable density threshold, not a philosophical category. Neither has been demonstrated yet. The site's self-audit has found zero confirmed predictions and the decisive galaxy test collapses to MOND (Modified Newtonian Dynamics: a 1983 rival idea that gravity gets slightly stronger than Newton predicts where it is very weak, instead of adding invisible dark matter). As one density function spanning every scale, it did not hold; the broader question of the frame is still open.
What happened, in three lines
- The proposal: one equation for how “crowding” makes the parts of a system act as one, from atoms to galaxies.
- It was tested against real data, mostly galaxies. Where it could be told apart from existing physics, it lost: 0 confirmed predictions, 6 refutations.
- What remains open is the bigger question behind it, and this site shows the whole attempt, failures included.
Beginner path: you can stop here and go to Step 2: First Encounter → The rest of this page is the technical detail, folded below.
For physicists: the technical details — the problem, the approach, what was tested, what failed (long; jargon is glossed in plain words where it first appears)
The Problem
Modern physics uses different equations for different scales. Quantum mechanics governs the small. General relativity governs the large. Chemistry sits in between with its own empirical rules. Consciousness has no physics at all.
This isn't necessarily wrong — specialized models work brilliantly in their domains. But it raises a question:
What if there's a single function that maps density to behavior across all scales?
The picture to hold onto (you'll see it again on First Encounter, the next step): a crowd milling around a plaza behaves like independent individuals; a marching band behaves like one organism. Synchronism's bet is that how densely packed the parts are is what moves a system from crowd-like to band-like — and that one dimmer-switch curve describes that shift everywhere, from electrons to galaxies.
The Approach
In plain English: Synchronism proposes one S-curve that smoothly goes from 0 (everything acting independently — the crowd) to 1 (everything locked together — the marching band) as density grows. There's a setting, γ, for how early that change starts — turn it down and the change stretches out; turn it up and it comes earlier, though never as a sudden snap — and a reference density that sets where on the curve a given system sits.
Show the equation (optional — the plain-English version above is the whole idea)
Synchronism proposes a coherence function: C(ρ) = tanh(γ · ln(ρ/ρcrit + 1)). It takes one input (density) and returns one output (coherence: 0 = sparse/independent, 1 = dense/collective). ⚠ “Coherence” here is not quantum coherence — superconductors and ultra-cold atom clouds (Bose–Einstein condensates), the textbook quantum-coherent materials, score low on this scale. In plain words: the formula γ = 2/√Ncorr gives a system with more parts moving together a flatter curve, so the most collective materials score lowest. Real superconductors switch on sharply, the opposite. So the low score is not a subtle definition; it is the formula getting collective systems backwards, and it is why that formula is marked audited-negative (γ regimes).
What “density” means, concretely. For a galaxy, ρ is the mass of stars and gas per cubic parsec at a given distance from the centre: about 0.6 M☉/pc³ in the middle of a disc like NGC 3198, falling outward. For a liquid or solid it is the ordinary mass (or particle) density. These are the same kind of number but on wildly different scales, so each sector needs its own reference density ρcrit to compare against (see one symbol, several knees). The consciousness pages do not compute C from a density at all.
tanh is the hyperbolic tangent — an S-shaped saturation function; over all inputs it spans (−1, +1), but the argument here is never negative, so C stays between 0 and 1. The γ parameter is the crowd→band dial from above; ρcrit is the reference density. The shape — tanh — is a phenomenological choice (plain words: picked because it matches the data, not because a deeper theory demands it), not a derived result: any S-curve with the same saturation properties would fit the same data equally well. (Full step-by-step breakdown: Equation Anatomy →)
The parameter γ = 2/√Ncorr is meant to depend only on how many particles are moving as a correlated unit: many correlated particles → small γ, few → large γ. It was meant as a sharpness, but it sets how early C rises; no γ makes the rise sudden. What makes a system sparse or dense is ρ, the other input. Two systems at the same density with different γ sit at different points on the curve; γ does not move them along the density axis.
And this mapping is the wrong way round. γ = 2/√Ncorr is badged audited-negative — sign-inverted for all collective systems: the real systems with the most correlated particles (superconductors, Bose–Einstein condensates) come out at the wrong end of the relation. So read the paragraph above as the framework's stated intent, not as an established result. The relation is also arguably empty rather than merely inverted, since Ncorr is never measured independently — it is always back-solved from a fitted γ, and the sign of a definition cannot be inverted. Details and a working calculator: γ Calculator →
For physicists: the fine print on γ and the S-curve (optional — skip freely on a first read)
Circularity caveat: The 1/√Ncorr scaling is a dimensional ansatz (an educated starting guess, shaped so the units work out) inspired by fluctuation theory — not a derivation from first principles. No counting protocol exists to derive Ncorr from a system's Hamiltonian (the equation describing all its interactions and energies) without first fitting γ to observed data. In practice, Ncorr is back-fit from γ — so γ has no independent predictive content beyond the calibration target. The γ Calculator states this explicitly. See γ Calculator →
The tanh shape is a phenomenological choice — a member of the compander family (short for compressor–expander: curves that squash a huge input range into a small output range, the way audio volume-levelling does; examples include μ-law audio companding, Hill/Naka–Rushton response functions, and Langevin/Curie–Weiss saturation). Any smooth S-curve with the same saturation properties would fit equally well; there is no variational principle or self-consistency equation that selects tanh specifically.
The log-density argument is physically motivated. Then tested against data. Some predictions held up. Others failed.
What We Tested — and How It Went
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This section was titled “What Worked” until 2026-07-17. The verdicts below were updated in place as tests were executed, and several are now Failed; the heading caught up with its own cards.Galaxy Rotation Curves
In plain words: the idea predicted that a galaxy's surroundings (crowded or empty space) would change how its outer stars move. It was checked on a public catalogue of 175 nearby galaxies whose spin speeds are measured carefully. The surroundings made no difference: the predicted effect was more than 20% of the scatter; the measured one was about 0.01%. One number did come out close, the acceleration scale where galaxies stop behaving like Newton's law (within 13%), but a 1983 rival idea, MOND, already explains that number. (MOND tweaks gravity itself instead of adding invisible dark matter; it is the benchmark this framework keeps being compared to, and losing to.)
Why “it turned into MOND” counts as a loss, not a win: MOND is a 40-year-old rival. When the galaxy formula is tuned to fit, it becomes MOND's own formula written in new symbols. Matching a rival exactly shows the new idea can reproduce the old one; it does not show the new idea adds anything. A theory earns its keep by predicting something the rival does not, and here it did not. (Added 2026-11-10 after a first-time reader asked why matching an existing theory was being called a failure.)
The detail, with survey names and statistics
Tested against 14,610 galaxies (175 SPARC + 14,435 ALFALFA–SDSS (quality cut)) — but the mechanism itself was tested on the 175 resolved SPARC rotation curves (SPARC is the catalogue above; ALFALFA–SDSS is a much larger sky survey with one rough spin speed per galaxy); the registered Tully–Fisher environment test (TEST-03, on the 14,585-galaxy cross-match) was never run as registered. a₀ = cH₀/(2π) reproduced within 13%, shared with MOND and other frameworks. The environment-dependent scatter prediction (TEST-03/05) has a corrected verdict as of 2026-07-15: the one real pattern found is that the scatter depends on a galaxy's shape (Hubble type; R²=0.14, p=5×10⁻⁶), on the SPARC-scale sample. Galaxy shape is not what was predicted, and it does not rescue the idea. The brief “MOND-shared” verdict (07-09) dissolved on adjudication: the frameworks' environment levers differ by orders of magnitude, so the axis discriminates, and the registered density-classified run now exists (research repo, 2026-07-14): r²=0.0001 against the framework's registered >20% claim. Refuted by execution. (This card said “14,760” until 2026-09-06 while the landing page called that figure incorrect; now rendered from one source. Rewritten in plain words 2026-10-19 after a simulated-visitor beginner persona met this run of survey names on Beginner step 1.)Chemistry: γ ≈ 1 Boundary
Most of 1,703 logged chemistry sessions (one “phenomenon type” each, a counter rather than a census) fitted near the γ≈1 boundary (sparse/independent ↔ dense/collective crossover). Sound velocity correlation: r = 0.982 — but each correlation is a fit, chosen after the data, against a quantity built from the material's Debye temperature, which already sets its sound speed. So they restate textbook solid-state physics (the Debye model, 1912), not this framework.
Note: C here measures collective ordering, not quantum phase coherence — quantum-coherent systems (superconductors, ultra-cold atom clouds) sit at low C due to their tiny γ.
What Failed
Melting Point Predictions
Why a galaxy equation cares about melting points: the bet was one density-to-coherence curve for every scale, so the same curve was tried on chemistry too. Average error: 53%. The coherence function doesn't capture enough crystal-specific physics for accurate melting points.
FailedSuperconductor Temperature
Superconductors are materials with zero electrical resistance below a certain temperature. For a well-studied one, the framework predicted that temperature at 607 K; the real value is 93 K — about 6.5× too high. And the extra factor the framework added for superconductors turned out to be a textbook 1960 formula written in new notation, so that part was not new physics either. (For experts: YBCO; the η “reachability” factor restates Abrikosov–Gor'kov pair-breaking — in plain words, the standard 1960 theory of how impurities break up the electron pairs that carry a supercurrent.)
Why two badges: the specific temperature prediction failed (the prediction ledger files it as refuted), while the extra factor behind it is a reparametrization — 22 of its 23 results restate standard condensed-matter physics. One is a wrong number; the other is not new physics. Neither softens the other.
The Research
3,308 autonomous research sessions. 42 complete research arcs. Conducted by autonomous AI agents, with a human (dp) setting direction and overseeing the ledger. The rule is that every prediction gets a kill criterion; in practice, 2 of 26 listed criteria are fully specified (the count). Every failure is documented.
This site is the public window into that research. Explore at whatever depth interests you.